"猫狗识别应用的模型构建与迁移学习探索"

lcome to the summary of the Machine Learning Nanodegree Graduation Project - Cat vs Dog Recognition Application. In this project, we aim to build a classification model to distinguish between images of cats and dogs. The data used in this project is primarily sourced from the Dogs vs Cats competition on Kaggle.com, which presents a binary classification problem in the field of computer vision. Although the competition has ended, we can still submit our prediction data to evaluate the loss score of our predictions using the leaderboard data from Kaggle. In addition to training a cat vs dog classification model using the competition data, we also intend to package the model into an application that can perform real-time cat and dog recognition. The primary goal of this project is to achieve higher accuracy in the cat vs dog recognition model, using the Kaggle leaderboard as a benchmark for model performance. Instead of reinventing the wheel, we plan to leverage existing pre-trained models for transfer learning and improve model accuracy through model ensembling, data augmentation, and fine-tuning of pre-trained models. To demonstrate the iterative process of model construction in this project, we will provide a detailed explanation of the model building process. We will be using Keras and Tensorflow to perform model transfer and construction. Once the model achieves the desired performance, we will develop it into a web application for real-time cat vs dog recognition. Overall, this project aims to showcase the exploration and development process of building a cat vs dog recognition model, from leveraging pre-trained models for transfer learning to creating a real-time recognition application. We will document the entire process and explain the insights gained from this project. Thank you for considering our Machine Learning Nanodegree Graduation Project - Cat vs Dog Recognition Application.